Study on multi-objective flexible job-shop scheduling problem considering energy consumption

Jiang, Zhenan; Zuo, Le; Mingcheng, E

doi:10.3926/jiem.1075

Cited by 29 publications

(15 citation statements)

References 19 publications

(20 reference statements)

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“…Since the importance of environmental protection in recent years, carbon emissions and energy consumption are considered in MOFJSP. Jiang et al [112] proposed a modified NSGA-II to solve the MOFJSP considering energy consumption. Yin et al [113] proposed a new low-carbon flexible job-shop mathematical scheduling model and addressed a multi-objective GA (MOGA) based on a simplex lattice design to solve this problem.…”

Section: Population-based Meta-heuristicsmentioning

confidence: 99%

Review on flexible job shop scheduling

Xie

Gao

Peng

et al. 2019

IET Collaborative Intelligent Manufacturing

142

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Flexible job shop scheduling problem (FJSP) is an NP‐hard combinatorial optimisation problem, which has significant applications in the real world. Due to its complexity and significance, lots of attentions have been paid to tackle this problem. In this study, the existing solution methods for the FJSP in recent literature are classified into exact algorithms, heuristics and meta‐heuristics, which are reviewed comprehensively. Moreover, the real‐world applications of the FJSP are also introduced. Finally, the development trends of the manufacturing industry are analysed, and the future research opportunities of the FJSP are summarised in detail.

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Section: Population-based Meta-heuristicsmentioning

confidence: 99%

Review on flexible job shop scheduling

Xie

Gao

Peng

et al. 2019

IET Collaborative Intelligent Manufacturing

142

View full text Add to dashboard Cite

show abstract

“…In recent years, as a special MOFJSP, energy-efficient FJSP also has attracted some attention. Jiang et al [41] proposed a blood-variation-based non-dominated sorting genetic algorithm-II (NSGA-II [42]). He et al [43] introduced a nested partitions algorithm.…”

Section: Introductionmentioning

confidence: 99%

An Imperialist Competitive Algorithm With the Diversified Operators for Many-Objective Scheduling in Flexible Job Shop

2019

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A number of related works have been done on multi-objective flexible job shop scheduling problem, however, many-objective flexible job shop scheduling problems (MaOFJSP) with at least four objectives are seldom considered. In this paper, an imperialist competitive algorithm with the diversified operators (DOICA) is proposed for MaOFJSP with the minimization of makespan, total tardiness, total workload, and total energy consumption. In DOICA, the diversified assimilation and revolution are used according to the features of empires, a novel imperialist competition is implemented by excluding the strongest empire and doing multiple neighborhood search of a solution in the strongest empire. The extensive experiments are conducted by using a number of benchmark instances to test the impact of strategies of DOICA on its performance and compare DOICA with other algorithms from literature finally. The computational results validate that the new strategies of DOICA are effective and DOICA can provide promising results for the considered MaOFJSP. INDEX TERMS Many-objective optimization, flexible job shop scheduling problem, imperialist competitive algorithm, diversified operators.

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“…One of the most effective scheduling approaches is the open production scheduling (OPS). Inspired by the traditional flexible jobshop scheduling problem (JSSP), the OPS is a novel scheduling method [23][24][25][26][27][28] that greatly enhances the processing efficiency of the entire system through the adoption of flexible processing route. Of course, the OPS raises a high demand for macro-optimization scheduling based on big data and features complex computation.…”

Section: Introductionmentioning

confidence: 99%

Simulation of Multivariate Scheduling Optimization for Open Production Line Based on Improved Genetic Algorithm

Li¹,

Gui²,

Xiao³

2018

Int. j. simul. model.

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This paper attempts to overcome the defects of bottleneck recognition and scheduling optimization in open production line. For this purpose, the author analysed the impact mechanism of external disturbances and system configuration changes on the production line, and put forward a multibottleneck identification model for production line through computer simulation. Then, the proposed model was applied to optimize the scheduling of open production line. Specifically, the bottlenecks of production line were identified based on hierarchical clustering and multi-attribute decision-making, aiming to overcome the small candidate set and low accuracy of traditional bottleneck identification algorithms. The measured results show that the proposed algorithm has clear primary and secondary logics; the number of main bottleneck clusters decreased with the increase in the order; the number of machines in the main bottleneck cluster changed nonlinearly. The traditional genetic algorithm (GA) was improved in three aspects: the local optimum trap was avoided by enhancing population diversity; the iteration speed was accelerated with the introduction of adaptive crossover operator and genetic operator; without sacrificing the computing speed, the convergence quality was guaranteed through the addition of multivariate competition algorithm. The research findings provide new insights into the efficient operation of production line.

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Study on multi-objective flexible job-shop scheduling problem considering energy consumption

Cited by 29 publications

References 19 publications

Review on flexible job shop scheduling

Review on flexible job shop scheduling

An Imperialist Competitive Algorithm With the Diversified Operators for Many-Objective Scheduling in Flexible Job Shop

Simulation of Multivariate Scheduling Optimization for Open Production Line Based on Improved Genetic Algorithm

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